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An Attention-Based Deep Network for Plant Disease Classification

Machine Graphics and Vision 2024年第1期33卷 47-67页

作者： Bera, Asish Bhattacharjee, Debotosh Krejcar, Ondrej Department of Computer Science and Information Systems Birla Institute of Technology and Science Rajasthan Pilani India Department of Computer Science and Engineering Jadavpur University West Bengal Kolkata India Center for Basic and Applied Science Faculty of Informatics and Management University of Hradec Králové Czech Republic Škoda Auto University Mladá Boleslav Czech Republic Universiti Teknologi Malaysia Kuala Lumpur Malaysia

Plant disease classification using machine learning in a real agricultural field environment is a difficult task. Often, an automated plant disease diagnosis method might fail to capture and interpret discriminatory information due to small variations among leaf sub-categories. Yet, modern Convolutional Neural Networks (CNNs) have achieved decent success in discriminating various plant diseases using leave images. A few existing methods have applied additional pre-processing modules or sub-networks to tackle this challenge. Sometimes, the feature maps ignore partial information for holistic description by part-mining. A deep CNN that emphasizes integration of partial descriptiveness of leaf regions is proposed in this work. The efficacious attention mechanism is integrated with high-level feature map of a base CNN for enhancing feature representation. The proposed method focuses on important diseased areas in leaves, and employs an attention weighting scheme for utilizing useful neighborhood information. The proposed Attention-based network for Plant Disease Classification (APDC) method has achieved state-of-the-art performances on four public plant datasets containing visual/thermal images. The best top-1 accuracies attained by the proposed APDC are: PlantPathology 97.74%, PaddyCrop 99.62%, PaddyDoctor 99.65%, and PlantVillage 99.97%. These results justify the suitability of proposed method. © 2024 institute of Information Technology, Warsaw University of Life Sciences - SGGW. All rights reserved.

关键词： Convolutional neural networks

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Don't Forget the Exception! : Considering Robustness Changes to Identify Design Problems 20

Don't Forget the Exception! : Considering Robustness Changes...

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20th IEEE/ACM International Conference on Mining Software Repositories, MSR 2023

作者： Oliveira, Anderson Correia, Joao Sousa, Leonardo Assuncao, Wesley K. G. Coutinho, Daniel Garcia, Alessandro Oizumi, Willian Barbosa, Caio Uchoa, Anderson Pereira, Juliana Alves Informatics Department - Pontifical Catholic Rio de Janeiro Brazil Carnegie Mellon University Department of Electrical and Computer Engineering Pittsburgh United States Institute of Software Systems Engineering Linz Austria Itapajé Brazil

ISBN: (纸本)9798350311846

Modern programming languages, such as Java, use exception-handling mechanisms to guarantee the robustness of software systems. Although important, the quality of exception code is usually poor and neglected by developers. Indiscriminate robustness changes (e.g., the addition of empty catch blocks) can indicate design decisions that negatively impact the internal quality of software systems. As it is known in the literature, multiple occurrences of poor code structures, namely code smells, are strong indicators of design problems. Still, existing studies focus mainly on the correlation of maintainability smells with design problems. However, using only these smells may not be enough since developers need more context (e.g., system domain) to identify the problems in certain scenarios. Moreover, these studies do not explore how changes in the exceptional code of the methods combined with maintainability smells can give complementary evidence of design problems. By covering both regular and exception codes, the developer can have more context about the system and find complementary code smells that reinforce the presence of design problems. This work aims to leverage the identification of design problems by tracking poor robustness changes combined with maintainability smells. We investigated the correlation between robustness changes and maintainability smells on the commit history of more than 160k methods from different releases of 10 open-source software systems. We observed that maintainability smells can be worsened or even introduced when robustness changes are performed. This scenario mainly happened for the smells Feature Envy, Long Method, and Dispersed Coupling. We also analyzed the co-occurrence between robustness and maintainability smells. We identified that the empty catch block and catch throwable robustness smells were the ones that co-occurred the most with maintainability smells related to the Concern Overload and Misplaced Concern design problems. T

关键词： Maintainability

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Career-Based Explainable Course Recommendation 18th

Career-Based Explainable Course Recommendation

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18th International Conference on Information for a Better World: Normality, Virtuality, Physicality, Inclusivity, iConference 2023

作者： Striebel, Jacob Myers, Rebecca Liu, Xiaozhong Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States Department of Computer Science Worcester Polytechnic Institute WorcesterMA01609 United States

ISBN: (纸本)9783031280313

The type and quality of education that a student receives can have a profound impact on their career. In contrast to education that is not intentionally organized to help students achieve specific career objectives, career-based education seeks to provide students with the skills that are required to achieve specific career goals. In this work, we propose a course recommendation framework that is designed to recommend courses based directly on their ability to teach skills relevant to a user-specified career goal. Within our framework, course recommendations are generated in a transparent manner, using skills to bridge between jobs and courses in a knowledge-based inference procedure. Due to the procedure’s transparency, our system is able to provide faithful template-based explanations detailing why each recommended course was chosen for recommendation. Our framework contrasts with other course recommendation systems in the literature that lack the ability to explain their choices and therefore may lack trustworthiness from the perspective of a user. The proposed framework has several applications, including assisting students with course planning, as well as aiding with curriculum evaluation and development by providing insight into the usefulness of specific courses to specific careers. We conduct a preliminary evaluation of our system, and its performance is competitive against two baselines. We provide all the resources needed to reproduce our results. (http://***/striebel/cbecr ) © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Students

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FC Portugal: RoboCup 2024 3D Simulation League Champions

FC Portugal: RoboCup 2024 3D Simulation League Champions

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27th RoboCup International Symposium, 2024

作者： Abreu, Miguel Mota, Pedro Azevedo, Tomás Silva, Francisco Reis, Luís Paulo Lau, Nuno Florido, Mário LIACC/LASI/FEUP Artificial Intelligence and Computer Science Lab Faculty of Engineering of the University of Port University of Porto Porto Portugal LIACC/LASI/FCUP Artificial Intelligence and Computer Science Lab Faculty of Sciences of the University of Porto University of Porto Porto Portugal IEETA/LASI/DETI Institute of Electronics and Informatics Engineering of Aveiro Department of Electronics Telecommunications and Informatics University of Aveiro Aveiro Portugal

ISBN: (纸本)9783031858581

FC Portugal, a simulated humanoid soccer team from the universities of Porto and Aveiro, achieved the third consecutive victory in the main competition of the 2024 RoboCup 3D Simulation League. The team registered 16 wins, 3 ties, and 2 losses, scoring a total of 105 goals while only conceding 17. FC Portugal also ended first in two secondary events: the Scientific Challenge, which evaluates innovative scientific contributions within the league, and the Fat Proxy Challenge, where teams share a common set of low-level skills and compete for the best high-level strategy. The codebase used in this competition was developed in Python after RoboCup 2021. This paper presents a brief overview of its structure along with the new features developed for this year’s competition. The key developments include a hierarchical learning framework, presented at the Scientific Challenge, and the release of our codebase to the RoboCup community. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： 3D modeling

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Classifying Upper Extremity Motor Function Using Deep Learning and Third-Person Video Data

Classifying Upper Extremity Motor Function Using Deep Learni...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Tran, Tan Chang, Lin-Ching Lum, Peter The Catholic University of America Department of Electrical Engineering and Computer Science WashingtonDC United States New Jersey Institute of Technology Department of Informatics NewarkNJ United States The Catholic University of America Department of Biomedical Engineering WashingtonDC United States

ISBN: (纸本)9798350362480

Post-stroke rehabilitation of upper extremity (UE) motor function is essential. Despite the widespread use of UE rehabilitation in clinical settings, assessing the success of these treatments is challenging. Methods for evaluating UE motor function include task performance under clinical supervision, patient self-reports, and data analysis from wearable devices equipped with accelerometers and gyroscopes. In prior research, we demonstrated that machine learning and deep learning models using data from a single wrist-worn accelerometer sensor could accurately differentiate between functional and non-functional UE movements in stroke patients. To overcome the limitations of single wrist-worn accelerometer sensors - challenges in capturing the full context of functional movements - this study presents a new deep learning (DL) based framework designed to classify functional and non-functional arm movements in videos captured from a conventional camera. The system is entirely automated and comprises two DL networks. The first network performs human pose estimation, extracting 2D pose key points of the paretic arm(s) and torso on 2-second sequences of the frames. The second network then uses these 2D pose landmarks to classify the movement sequence as functional or non-functional. This system offers two key benefits for rehabilitation. First, it can automatically generate initial annotations for video frames, significantly reducing the time needed for manual labeling. Second, analyzing the effectiveness of UE rehabilitation through third-person videos allows for objective outcome measurement of UE treatments in stroke survivors' home environments. © 2024 IEEE.

关键词： Accelerometers

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A Robust Preprocessing Method for Measuring Image Visual Quality Using Log-Polar FFT Features 8th

A Robust Preprocessing Method for Measuring Image Visual Qua...

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8th International Congress on Artificial Intelligence and Pattern Recognition, IWAIPR 2023

作者： Chen, Guang Yi Krzyzak, Adam Valev, Ventzeslav Department of Computer Science and Software Engineering Concordia University MontrealQCH3G 1M8 Canada Institute of Mathematics and Informatics Bulgarian Academy of Sciences Sofia1113 Bulgaria

ISBN: (纸本)9783031495519

To register two images, existing methods normally estimate the parameters of an affine transform, and then perform an inverse affine transform to the test image by using the estimated parameters. Any metric can then be applied to the reference image and the normalized image. In this paper we propose a new method for measuring visual image quality which circumvents the need to estimate the parameters of the affine transform. Instead, we use the log-polar transform and the fast Fourier transform (FFT) to extract features that are invariant to translation, rotation, and scaling. We apply the existing structural similarity index measure (SSIM) to the two invariant feature images, where no inverse transform is needed. Experimental results show that our proposed method outperforms the standard metric, the mean SSIM (MSSIM), significantly in terms of visual quality scores. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： image visual quality quality metrics rotation invariance scale invariance translation invariance

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Determination of Admissible Power and Digital Filtering Algorithm Development of Discretely Regulated Autonomous Induction Generators 4

Determination of Admissible Power and Digital Filtering Algo...

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4th IEEE KhPI Week on Advanced Technology, KhPI Week 2023

作者： Shurub, Yuriy Morozov-Leonov, Oleksandr Department of Electromechanical Systems Institute of Electrodynamics of the National Academy of Sciences of Ukraine Kiev Ukraine Department of Informatics and Computer Engineering National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" Kiev Ukraine

ISBN: (纸本)9798350395532

Autonomous induction generators have such advantages as simplicity of design and high reliability. For a number of generators based on general industrial induction motors, their admissible capacities were determined according to the criterion of permissible thermal load of the stator winding. A digital filtering algorithm was developed for discrete adjustment of the capacitance of the excitation system under the conditions of stochastic load, which allows to avoid rattling of contacts at switching points. © 2023 IEEE.

关键词： Induction motors

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Macular Holes Detection Using Deep Learning on Optical Coherence Tomography Images

Macular Holes Detection Using Deep Learning on Optical Coher...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Hung, Che-Lun Lin, Keng-Hung Lee, Yu-Kai Lin, ChunHsien Tsai, Yin-Te Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taiwan Taichung Veterans General Hospital Department of Ophthalmology Taiwan Providence University Department of Computer Science and Information Engineering Taiwan Providence University Department of Computer Science and Communication Engineering Taiwan

ISBN: (纸本)9798350337488

Macular holes (MHs) can be either idiopathic or secondary to a result of concurrent or previous pathology such as ocular inflammation, trauma or surgery. Idiopathic macular holes may eventually impair the life quality and self-care capability of patients. In clinical, the optical coherence tomography (OCT) images of macular holes can be divided into 4 stages based upon the size of macular hole. The size of macular hole is inversely influencing surgical success rate and visual outcomes. Minimizing human judgment errors in the classification of MHs and enhancing the overall quality of classification are critical objectives. In this paper, we develop a deep learning algorithm to detect MHs on OCT images and also propose an automatic algorithm to measure the size of MH. The MH detection algorithm demonstrates exceptional accuracy, while the measurement algorithm offers superior efficiency when compared to the conventional caliper-based method utilized with spectral-domain OCT devices - a time-consuming procedure for ophthalmologists. These advancements promise to expedite the diagnostic process and facilitate to rapidly recognize the stage 2 MHs from stage 3 or 4 cases. © 2023 IEEE.

关键词： Deep Learning Macular Holes OCT images

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Text Analysis and Recognition of Emotional Content Using Deep Learning Methods and BERT

Text Analysis and Recognition of Emotional Content Using Dee...

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International Conference on computer and Information Science (ACIS)

作者： Efthymios Andrikakis Isidoros Perikos Michael Paraskevas Ioannis Hatzilygeroudis Department of Computer Engineering & Informatics University of Patras Patras Greece Department of Computer Engineering & Informatics University of Patras and Computer Technology Institute and Press “Diophantus” Patras Greece

Recognizing the emotional content of Natural Language sentences can improve the way humans communicate with a computer system by enabling them to recognize and imitate emotional expressions. In this paper, deep learning, deep neural networks as well as Transformers were examined. Specifically, we designed and developed deep learning methods and BERT-based implementations for recognizing emotional content in user-generated data. Extensive experiments were conducted using these models on a variety of textual data and all the designed methods were evaluated. The results of the study show that BERT achieved the best overall performance, getting an F1-score of 0.86 on the Twitter Data dataset. Also, the Bi-LSTM with Attention Mechanism and Bi-LSTM using Word2vec performed quite well, achieving F1-scores of 0.83 and 0.82, respectively.

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Comparative Analysis of Movie Recommendation Systems Using Filtering Techniques on IMDB and Rotten Tomatoes 15

Comparative Analysis of Movie Recommendation Systems Using F...

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15th International Conference on Information, Intelligence, Systems and Applications, IISA 2024

作者： Majhi, Santosh Kumar Sahani, Preetipadma Pradhan, Rosy Acharya, Biswaranjan Grivokostopoulou, Foteini Kanavos, Andreas Gerogiannis, Vassilis C. Department of Computer Science and Information Technology Bilaspur India Veer Surendra Sai University of Technology Department of Computer Science and Engineering Odisha Burla India Veer Surendra Sai University of Technology Department of Electrical Engineering Odisha Burla India Marwadi University Department of Computer Engineering Ai Rajkot India Computer Technology Institute and Press 'Diophantus' Patras Greece Ionian University Department of Informatics Corfu Greece University of Thessaly Department of Digital Systems Larissa Greece

ISBN: (纸本)9798350368833

Recommendation systems are pivotal in industries like entertainment, where competitiveness is intense. This study enhances movie recommendations by utilizing rich datasets from IMDB and Rotten Tomatoes, incorporating movie ratings, user profiles, audience scores, and critics' reviews. These elements were subjected to sentiment analysis to refine the data quality. We explored three filtering techniques: content-based, which analyzes movie features;collaborative, which processes user behavior patterns;and hybrid, combining both to leverage their strengths. The effectiveness of these methods was assessed using Mean Average Precision at k (MAP-k) and Root Mean Square Error (RMSE), with our results showing notable improvements in recommendation accuracy. For instance, the hybrid method achieved a MAP ©k of 0.271 and an RMSE of 0.634, indicating enhanced precision and reliability of the recommendations. These findings suggest significant potential for improving user satisfaction and retention in digital platforms, demonstrating the benefit of integrating diverse data sources and analytical techniques in recommendation systems.

关键词： Collaborative filtering

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